At the foundation of most recommendation systems is a graph knowledge component which provides how content is intra directed and the weight given to each content. However, typically implementation of this feature utilizes a large and always available set of content that is relatively static. A content graph built from the similarity of their metadata is traditionally performed by a set-intersection algorithm. If the set size is N, each content will have a graph value list of N−1 and their associated weight. This graph can be used to build a list of similar content if given seed content. Thus, the calculation of this graph carries a high initial load, but decreases over time as new content is added and little, if any, content is deleted or otherwise made not available.
There is a need to develop systems and methods for a recommendation system that is useful for ever-changing content, much of which is available only during specific times of the day.